Evaluation of Random Forest Model
Row
Evaluation of Accuracy
Row
Cross-Validation Accuracy vs Number of Random Predictors
Confusion Matrix
$table
Reference
Prediction A B C D E
A 1674 0 0 0 0
B 7 1127 5 0 0
C 0 5 1019 2 0
D 0 0 7 954 3
E 0 0 0 2 1080
$overall
Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
0.9947324 0.9933361 0.9925313 0.9964182 0.2856415
AccuracyPValue McnemarPValue
0.0000000 NaN Evaluation of Boosting Model
Accuracy vs Boosting Iterations
Confusion Matrix
$table
Reference
Prediction A B C D E
A 1656 10 3 5 0
B 38 1060 39 2 0
C 0 35 979 9 3
D 1 4 32 918 9
E 7 11 9 15 1040
$overall
Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
9.605777e-01 9.501083e-01 9.552874e-01 9.654049e-01 2.892099e-01
AccuracyPValue McnemarPValue
0.000000e+00 7.638490e-09
$byClass
Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
Class: A 0.9729730 0.9956969 0.9892473 0.9890762 0.9892473
Class: B 0.9464286 0.9834208 0.9306409 0.9873578 0.9306409
Class: C 0.9218456 0.9902550 0.9541910 0.9829183 0.9541910
Class: D 0.9673340 0.9906807 0.9522822 0.9937005 0.9522822
Class: E 0.9885932 0.9913097 0.9611830 0.9975016 0.9611830
Recall F1 Prevalence Detection Rate Detection Prevalence
Class: A 0.9729730 0.9810427 0.2892099 0.2813934 0.2844520
Class: B 0.9464286 0.9384683 0.1903144 0.1801189 0.1935429
Class: C 0.9218456 0.9377395 0.1804588 0.1663551 0.1743415
Class: D 0.9673340 0.9597491 0.1612574 0.1559898 0.1638063
Class: E 0.9885932 0.9746954 0.1787596 0.1767205 0.1838573
Balanced Accuracy
Class: A 0.9843349
Class: B 0.9649247
Class: C 0.9560503
Class: D 0.9790074
Class: E 0.9899515Evaluation of Support Vector Machine Model
Row
Diagnostics for Linear Support Vector Machine
Support Vector Machine object of class "ksvm"
SV type: C-svc (classification)
parameter : cost C = 1
Linear (vanilla) kernel function.
Number of Support Vectors : 7225
Objective Function Value : -1450.051 -1275.306 -1046.239 -628.1268 -1326.171 -881.7941 -1774.954 -1207.525 -1037.249 -1209.01
Training error : 0.210526 Diagnostics for Radial Support Vector Machine
Support Vector Machine object of class "ksvm"
SV type: C-svc (classification)
parameter : cost C = 1
Gaussian Radial Basis kernel function.
Hyperparameter : sigma = 0.0138342908497241
Number of Support Vectors : 7042
Objective Function Value : -1128.491 -836.3012 -734.9416 -438.009 -1048.849 -589.373 -761.5491 -1012.77 -718.7094 -628.9886
Training error : 0.068501 Row
Confusion Matrix Linear Support Vector Machine
$table
Reference
Prediction A B C D E
A 1550 27 34 55 8
B 143 824 68 22 82
C 92 94 804 19 17
D 68 30 112 707 47
E 73 131 70 52 756
$overall
Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
7.886151e-01 7.310874e-01 7.779569e-01 7.989865e-01 3.272727e-01
AccuracyPValue McnemarPValue
0.000000e+00 3.543329e-53 Confusion Matrix Radial Support Vector Machine
$table
Reference
Prediction A B C D E
A 1660 5 7 2 0
B 87 997 52 1 2
C 4 41 956 24 1
D 6 3 105 849 1
E 6 13 57 24 982
$overall
Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
9.250637e-01 9.050519e-01 9.180390e-01 9.316635e-01 2.995752e-01
AccuracyPValue McnemarPValue
0.000000e+00 2.353217e-41